This paper proposes an age progression method for facial videos. Age is one of the main factors that changes the appearance of the face, due to the associated sagging, spots, and wrinkles. These aging features change in appearance depending on facial expressions. As an example, we see wrinkles appear in the face of the young when smiling, but the shape of wrinkles changes in older faces. Previous work has not considered the temporal changes of the face, using only static images as databases. To solve this problem, we extend the texture synthesis approach to use facial videos as source videos. First, we spatio-temporally align the videos of database to match the sequence of a target video. Then, we synthesize an aging face and apply the temporal changes of the target age to the wrinkles appearing in the facial expression image in the target video. As a result, our method successfully generates expression changes specific to the target age.